0. Data Import and Cleaning

Data Import

videogames.df <- read.csv(file.path(project.dir, dataset.dir, 'vgsales-12-4-2019.csv'))
colnames(videogames.df)
##  [1] "Rank"           "Name"           "basename"       "Genre"         
##  [5] "ESRB_Rating"    "Platform"       "Publisher"      "Developer"     
##  [9] "VGChartz_Score" "Critic_Score"   "User_Score"     "Total_Shipped" 
## [13] "Global_Sales"   "NA_Sales"       "PAL_Sales"      "JP_Sales"      
## [17] "Other_Sales"    "Year"           "Last_Update"    "url"           
## [21] "status"         "Vgchartzscore"  "img_url"

Data cleaning

# Since the data was collected in April of 2019, we are excluding games with year = 2019 since it does not give a comprehensive picture of all the sales during 2019. 
videogames.clean <- videogames.df %>% filter(Year < 2019)

# E was originally called KA for ESRB ratings, so we are going to make all the KA ratings E
videogames.clean <- videogames.clean %>% mutate(ESRB_Rating = replace(ESRB_Rating, ESRB_Rating=='KA', 'E'))

# Make give the ESRB rating levels for easier graphing/ data manipulation
unique(videogames.clean$ESRB_Rating)
## [1] "E"   ""    "M"   "E10" "T"   "RP"  "EC"  "AO"
videogames.clean$ESRB_Rating <- factor(videogames.clean$ESRB_Rating,levels = c('','RP','E', 'EC', 'E10','T','M','AO'))

Data reshaping

We want to compare sales across different regions, so it would be convenient to have one column ‘region’ and then a corresponding column for sales in USD (millions).

vs_byregion <- videogames.clean %>% gather(Region, Sales, Global_Sales:Other_Sales, na.rm = T)

1. Descriptive analysis

Conduct some descriptive analysis on the data, figuring out: * distributions of variables, * variables that appear to be strongly related with each other (using appropriate methods to quantify the relationships based on whether variables are numerical or categorical).

Distributions

Sales

From the boxplot we can see that we have 2 extreme outliers. After investigating, it looks like two outliers are GTA V (ps3 and ps4)

boxplot(videogames.clean$Global_Sales, xlab = 'Global Sales (millions of USD)')

videogames.clean[which(videogames.clean$Global_Sales > 17), ]
hist(videogames.clean$Global_Sales,
     xlab = 'Global Sales (millions of USD)',
     xlim = c(0, .5),
     breaks = 2000)

Platform

ggplotly(
videogames.clean %>% 
  count(Platform, sort = TRUE) %>% 
  ggplot(aes(x = reorder(Platform, -n), y = n)) +
  geom_bar(stat = "identity",position = position_dodge(width=0)) +
  theme(axis.text.x=element_text(angle=90,hjust=1, vjust = 0.5))
)

ESRB Rating

videogames.clean %>% ggplot(aes(x = ESRB_Rating)) +
  geom_bar()

Genre

videogames.clean %>% 
  count(Genre, sort = TRUE) %>% 
  ggplot(aes(x = reorder(Genre, -n), y = n)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x=element_text(angle=45,hjust=1))

Scoring

Here we looked at distribution of User Scores and Critic Scores as well as the average Critic and User Score over time.

videogames.clean %>% ggplot() +
  geom_histogram(binwidth = 0.5,aes(x = Critic_Score, fill = 'pink') ) +
  geom_histogram(binwidth = 0.5,aes(x = User_Score, fill = 'blue') )

Publisher

We have a ton of publishers

Year

videogames.clean %>% ggplot(aes(x = Year)) +
  geom_bar()

Notable Correlations

Sales by year

vs_sales.byregion.byyear <- vs_byregion %>% group_by(Year, Region)  %>% summarize(Sales = sum(Sales))
vs_sales.byregion.byyear %>% ggplot(aes(x=Year, y= Sales))+
  geom_line(aes(color = Region))

Sales per platform

Sales per rating

Sales per genre

ESRB Rating per year

Rating by year

videogames.clean %>% group_by(Year) %>% summarise(
  User_Score = mean(User_Score, na.rm = T),
  Critic_Score = mean(Critic_Score, na.rm = T), 
  Vgchartzscore = mean(Vgchartzscore, na.rm = T)) %>% 
  filter(Year >= 1989) %>%
  mutate(User_Score2 = case_when(Year >= 1996 ~ User_Score,
                           TRUE ~ NaN)) %>%
  gather(ScoreType, Score, c(User_Score,Critic_Score,Vgchartzscore), na.rm = T) %>%
  
  ggplot(aes(x = Year)) + # TODO : Make look better
  geom_line(aes(y = Score, color = ScoreType)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
  scale_x_continuous('ID', labels = 1980:2018, breaks = 1980:2018) +
  xlab('ID') +
  xlim(1989, 2018
  )

2. One sample stats inference for response variable of interest (sales) bib

⁃   construct CI for population mean value for sales
⁃
⁃   assumptions:
⁃   sample is randomized: we will pick a random sample from our data
⁃   population distribution is approximately normal: 
⁃   NO, very left skewed, but we will use large sample sizes so that will not be too much of a problem
⁃   Extreme outliers: 
⁃   we will remove extreme outliers (GTA V)

3. Construct confidence interval for proportion of video games in a certain genre (genre TBD)

5. We will (c) construct a confidence interval for population correlation between critic score and sales using bootstrap.